title: Development of uncertainty-based work injury model using Bayesian structural equation modelling.
abstract: This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.
authors.affiliation: a Department of Mining Engineering , National Institute of Technology Rourkela , Orissa - 769008 , India.
authors.forename: Snehamoy
authors.initials: S
authors.lastname: Chatterjee
mesh_terms: D001499:Bayes Theorem; D003032:Coal Mining / Q000706:statistics & numerical data; D006801:Humans; D007194:India / Q000453:epidemiology; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D060051:Occupational Injuries / Q000453:epidemiology* / Q000209:etiology; D035501:Uncertainty
keywords: Bayesian modelling; coal mines; human factors; injury; multivariate statistics